Dental navigation systems are increasingly common and commercially available. Many existing dental navigation systems, such as that described in U.S. Pat. No. 9,125,624 and U.S. Pat. No. 9,402,691 involve a three-dimensional (3D) coordinate mapping between locations in a subject jaw, that is, the patient's jaw that is being optically tracked, and homologous locations in a pre-acquired volumetric computed tomography (CT) image of that subject jaw.
The parameters of the 3D coordinate mapping are commonly in the form of a matrix, representing a rigid 3D coordinate transform (the “registration mapping”) between a reference coordinate space associated with the subject jaw and a reference coordinate space associated with the CT image. The matrix can be computed using an algorithmic process called registration.
Existing methods for registering a rigid object, such as a human jaw, are generally based on some combination of points-points, points-surface and surface-surface alignment. The use of fiducials (i.e., an artificial object of known geometry with boundaries that can be reliably detected in the image) enables a high degree of registration reliability and accuracy, but incurs an undesirable time and cost overhead.
Some prior art publications describe a fiducial-free registration of a jaw with its image. For example, U.S. Patent Publication No. 2017/0290554 discloses a system that initially aligns landmark trace points to the surface of the teeth and then uses an iterative closest point (ICP) algorithm to refine the alignment between trace points and the surface of the teeth. The surface geometry is extracted from the CT image using an iso-surface following algorithm (i.e., “marching cubes”) and the points are collected using a position-tracked calibrated probe with a spherical tip. Following a probe tip calibration and the attachment of a trackable thermoplastic fixture to the teeth as a reference, a point-to-point registration is used to provide an initial approximate alignment of the jaw with the CT image. In a subsequent refinement step, an operator performs a long trace over the surface of the teeth and the computer iteratively adjusts the registration mapping to reduce the average distance between the trace and the previously extracted iso-surface.
Existing dental navigation systems and methods referenced above work reasonably well on models, but frequently fail when applied to patient jaws because the surface geometry obtained by iso-surface segmentation, or other automated segmentation algorithm applied to the commonly used cone-beam CT (CBCT) images, can be highly unreliable and distorted due to a variety of reasons. First, non-uniformities in the intensities can be present across the imaged field. Second, streak artifacts are often present near high-density regions of the CT image. As well, non-uniformities due to varying teeth density or artificial materials introduced to the mouth from previous dental treatments can also cause distortions. Furthermore, since the time delay between from the CT scan to the surgery can be several months, teeth movement can occur within this time to render some portions the CT image an unreliable depiction of the jaw surface during surgery. Finally, movement of the patients' jaw during scanning can also cause geometrical distortions in the CT image.
Another major challenge with existing dental navigation systems and methods is the attachment of the fiducial to the jaw, which typically covers three or more teeth. Coverage of three or more teeth substantially reduces the exposed teeth surfaces available for the registration of the alignment of the jaw with the CT image.